Logo PTI Logo FedCSIS

Proceedings of the 16th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 25

Towards Objectification of Multi-Criteria Assessments: a Comparative Study on MCDA Methods

, , ,

DOI: http://dx.doi.org/10.15439/2021F61

Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 417–425 ()

Full text

Abstract. Objective evaluation in problems considering many, often conflicting criteria is challenging for the decision-maker. This paper presents an approach based on MCDA methods to objectify evaluations in the camera selection problem. The proposed approach includes three MCDA methods, TOPSIS, VIKOR, COMET, and two criterion weighting techniques. Two ranking similarity coefficients were used to compare the resulting rankings of the alternatives: WS and r\_w. The performed research confirmed the importance of the appropriate selection of multi-criteria decision-making methods for the solved problem and the relevance of comparative analysis in method selection and construction of objective rankings of alternatives.

References

  1. B. Kizielewicz, J. Wątróbski, and W. Sałabun, “Identification of Relevant Criteria Set in the MCDA Process—Wind Farm Location Case Study,” Energies, vol. 13, no. 24, p. 6548, 2020. http://dx.doi.org/https://doi.org/10.3390/en13246548
  2. J. Watróbski, P. Ziemba, and W. Wolski, “MCDA-based decision support system for sustainable management-RES case study,” in 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk, Poland, 11–14 September 2016. IEEE, 2016. http://dx.doi.org/https://doi.org/10.15439/2016F489 pp. 1235–1239.
  3. C. Beaudrie, C. J. Corbett, T. A. Lewandowski, T. Malloy, and X. Zhou, “Evaluating the Application of Decision Analysis Methods in Simulated Alternatives Assessment Case Studies: Potential Benefits and Challenges of using MCDA,” Integrated Environmental Assessment and Management, vol. 17, no. 1, pp. 27–41, 2021. http://dx.doi.org/https://doi.org/10.1002/ieam.4316
  4. A. Karczmarczyk, J. Wątróbski, and J. Jankowski, “Comparative Study of Different MCDA-Based Approaches in Sustainable Supplier Selection Problem,” in Information Technology for Management: Emerging Research and Applications. Springer, 2018. http://dx.doi.org/https://doi.org/10.1007/978-3-030-15154-6_10 pp. 176–193.
  5. N. Tsotsolas and S. Alexopoulos, “MCDA Approaches for Efficient Strategic Decision Making,” in Preference Disaggregation in Multiple Criteria Decision Analysis. Springer, 2018. http://dx.doi.org/https://doi.org/10.1007/978-3-319-90599-0_2 pp. 17–58.
  6. W. Sałabun, J. Watróbski, and A. Shekhovtsov, “Are MCDA Methods Benchmarkable? A Comparative Study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II Methods,” Symmetry, vol. 12, no. 9, p. 1549, 2020. http://dx.doi.org/https://doi.org/10.3390/sym12091549
  7. M. Cinelli, M. Kadziński, M. Gonzalez, and R. Słowiński, “How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy,” Omega, p. 102261, 2020. http://dx.doi.org/https://doi.org/10.1016/j.omega.2020.102261
  8. A. Papapostolou, F. D. Mexis, E. Sarmas, C. Karakosta, and J. Psarras, “Web-based Application for Screening Energy Efficiency Investments: A MCDA Approach,” in 2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA), Piraeus, Greece, 15–17 July 2020. IEEE, 2020. http://dx.doi.org/https://doi.org/10.1109/IISA50023.2020.9284403 pp. 1–7.
  9. J. Wątróbski, J. Jankowski, P. Ziemba, A. Karczmarczyk, and M. Zioło, “Generalised framework for multi-criteria method selection,” Omega, vol. 86, pp. 107–124, 2019. http://dx.doi.org/https://doi.org/10.1016/j.omega.2018.07.004
  10. R. K. Dhurkari, “MCGL: a new reference dependent MCDM method,” International Journal of Operational Research, vol. 36, no. 4, pp. 477–495, 2019. http://dx.doi.org/https://doi.org/10.1504/IJOR.2019.104053
  11. B. Roy, “Classement et choix en présence de points de vue multiples,” Revue française d’informatique et de recherche opérationnelle, vol. 2, no. 8, pp. 57–75, 1968. http://dx.doi.org/http://www.numdam.org/item?id=RO_1968__2_1_57_0
  12. J.-P. Brans and P. Vincke, “Note—a preference ranking organisation method: (the promethee method for multiple criteria decision-making),” Management science, vol. 31, no. 6, pp. 647–656, 1985. http://dx.doi.org/https://doi.org/10.1287/mnsc.31.6.647
  13. J.-M. Martel and B. Matarazzo, “Other outranking approaches,” in Multiple criteria decision analysis: state of the art surveys. Springer, 2005. http://dx.doi.org/https://doi.org/10.1007/978-1-4939-3094-4_7 pp. 197–259.
  14. A. Darko, A. P. C. Chan, E. E. Ameyaw, E. K. Owusu, E. Pärn, and D. J. Edwards, “Review of application of analytic hierarchy process (ahp) in construction,” International journal of construction management, vol. 19, no. 5, pp. 436–452, 2019. http://dx.doi.org/https://doi.org/10.1080/15623599.2018.1452098
  15. C.-L. Hwang and K. Yoon, “Methods for multiple attribute decision making,” in Multiple attribute decision making. Springer, 1981. http://dx.doi.org/https://doi.org/10.1007/978-3-642-48318-9_3 pp. 58–191.
  16. L. Duckstein and S. Opricovic, “Multiobjective optimization in river basin development,” Water resources research, vol. 16, no. 1, pp. 14–20, 1980. http://dx.doi.org/https://doi.org/10.1029/WR016i001p00014
  17. N. Sinha, N. Priyanka, and P. Joshi, “Using spatial multi-criteria analysis and ranking tool (SMART) in earthquake risk assessment: A case study of Delhi region, India,” Geomatics, Natural Hazards and Risk, vol. 7, no. 2, pp. 680–701, 2016. http://dx.doi.org/https://doi.org/10.1080/19475705.2014.945100
  18. S. Greco, “A new PCCA method: Idra,” European Journal of Operational Research, vol. 98, no. 3, pp. 587–601, 1997. http://dx.doi.org/https://doi.org/10.1016/S0377-2217%2896%2900022-7
  19. H. Voogd, “Multicriteria evaluation with mixed qualitative and quantitative data,” Environment and Planning B: Planning and Design, vol. 9, no. 2, pp. 221–236, 1982. http://dx.doi.org/https://doi.org/10.1068/b090221
  20. A. Giarlotta, “Passive and active compensability multicriteria annalysis (PACMAN),” Journal of Multi-Criteria Decision Analysis, vol. 7, no. 4, pp. 204–216, 1998. http://dx.doi.org/https://doi.org/10.1002/%28SICI%291099-1360%28199807%297:4%3C204::AID-MCDA192%3E3.0.CO%3b2-5
  21. P. Fortemps, S. Greco, and R. Słowiński, “Multicriteria choice and ranking using decision rules induced from rough approximation of graded preference relations,” in International Conference on Rough Sets and Current Trends in Computing, Uppsala, Sweden, 1–5 June 2004. Springer, 2004. http://dx.doi.org/https://doi.org/10.1007/978-3-540-25929-9_62 pp. 510–522.
  22. W. Sałabun, “The Characteristic Objects Method: A New Distance-based Approach to Multicriteria Decision-making Problems,” Journal of Multi-Criteria Decision Analysis, vol. 22, no. 1-2, pp. 37–50, 2015. http://dx.doi.org/https://doi.org/10.1002/mcda.1525
  23. W. Sałabun and A. Piegat, “Comparative analysis of MCDM methods for the assessment of mortality in patients with acute coronary syndrome,” Artificial Intelligence Review, vol. 48, no. 4, pp. 557–571, 2017. http://dx.doi.org/https://doi.org/10.1007/s10462-016-9511-9
  24. J. Watróbski and J. Jankowski, “Knowledge management in MCDA domain,” in 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), Lodz, Poland, 13–16 September 2015. IEEE, 2015. http://dx.doi.org/https://doi.org/10.15439/2015F295 pp. 1445–1450.
  25. A. Karczmarczyk, J. Wątróbski, G. Ladorucki, and J. Jankowski, “MCDA-based approach to sustainable supplier selection,” in 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), Poznan, Poland, 9–12 September 2018. IEEE, 2018. http://dx.doi.org/https://doi.org/10.15439/2018F336 pp. 769–778.
  26. V. Yadav, S. Karmakar, P. P. Kalbar, and A. K. Dikshit, “PyTOPS: A Python based tool for TOPSIS,” SoftwareX, vol. 9, pp. 217–222, 2019. http://dx.doi.org/https://doi.org/10.1016/j.softx.2019.02.004
  27. T. Imandasari, M. G. Sadewo, A. P. Windarto, A. Wanto, H. O. L. Wijaya, and R. Kurniawan, “Analysis of the Selection Factor of Online Transportation in the VIKOR Method in Pematangsiantar city,” in Journal of Physics: Conference Series, Niagara Hotel, Parapat, Indonesia, 10–12 October 2018, vol. 1255, no. 1. IOP Publishing, 2019. http://dx.doi.org/https://doi.org/10.1088/1742-6596/1255/1/012008 p. 012008.
  28. A. Mardani, E. K. Zavadskas, K. Govindan, A. Amat Senin, and A. Jusoh, “VIKOR technique: A systematic review of the state of the art literature on methodologies and applications,” Sustainability, vol. 8, no. 1, p. 37, 2016. http://dx.doi.org/https://doi.org/10.3390/su8010037
  29. D. Siregar, H. Nurdiyanto, S. Sriadhi, D. Suita, U. Khair, R. Rahim, D. Napitupulu, A. Fauzi, A. Hasibuan, M. Mesran et al., “Multi-attribute decision making with VIKOR method for any purpose decision,” in Journal of Physics: Conference Series, Kuching, Sarawak, Malaysia, 25–27 November 2017, vol. 1019, no. 1. IOP Publishing, 2018. http://dx.doi.org/https://doi.org/10.1088/1742-6596/1019/1/012034 p. 012034.
  30. M. Kumar and C. Samuel, “Selection of best renewable energy source by using VIKOR method,” Technology and Economics of Smart Grids and Sustainable Energy, vol. 2, no. 1, p. 8, 2017. http://dx.doi.org/https://doi.org/10.1007/s40866-017-0024-7
  31. J. Wątróbski, W. Sałabun, A. Karczmarczyk, and W. Wolski, “Sustainable decision-making using the COMET method: An empirical study of the ammonium nitrate transport management,” in 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), Prague, Czech Republic, 3–6 September 2017. IEEE, 2017. http://dx.doi.org/https://doi.org/10.15439/2017F455 pp. 949–958.
  32. W. Sałabun, J. Wątróbski, and A. Piegat, “Identification of a multi-criteria model of location assessment for renewable energy sources,” in International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland, 12–16 June 2016. Springer, 2016. doi: https://doi.org/10.1007/978-3-319-39378-0_28 pp. 321–332.
  33. W. Sałabun, P. Ziemba, and J. Wątróbski, “The rank reversals paradox in management decisions: The comparison of the AHP and COMET methods,” in International Conference on Intelligent Decision Technologies, Tenerife, Spain, 15–17 June 2016. Springer, 2016. http://dx.doi.org/https://doi.org/10.1007/978-3-319-39630-9_15 pp. 181–191.
  34. W. Sałabun, A. Karczmarczyk, J. Wątróbski, and J. Jankowski, “Handling data uncertainty in decision making with COMET,” in 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, 18–21 November 2018. IEEE, 2018. http://dx.doi.org/https://doi.org/10.1109/SSCI.2018.8628934 pp. 1478–1484.
  35. A. Shekhovtsov, J. Kołodziejczyk, and W. Sałabun, “Fuzzy Model Identification Using Monolithic and Structured Approaches in Decision Problems with Partially Incomplete Data,” Symmetry, vol. 12, no. 9, p. 1541, 2020. http://dx.doi.org/https://doi.org/10.3390/sym12091541
  36. H. Li, W. Wang, L. Fan, Q. Li, and X. Chen, “A novel hybrid MCDM model for machine tool selection using fuzzy DEMATEL, entropy weighting and later defuzzification VIKOR,” Applied Soft Computing, vol. 91, p. 106207, 2020. http://dx.doi.org/https://doi.org/10.1016/j.asoc.2020.106207
  37. N. Yalcin and U. Ünlü, “A multi-criteria performance analysis of Initial Public Offering (IPO) firms using CRITIC and VIKOR methods,” Technological and Economic development of Economy, vol. 24, no. 2, pp. 534–560, 2018. http://dx.doi.org/http://dx.doi.org/10.3846/20294913.2016.1213201
  38. A. Shekhovtsov, V. Kozlov, V. Nosov, and W. Sałabun, “Efficiency of Methods for Determining the Relevance of Criteria in Sustainable Transport Problems: A Comparative Case Study,” Sustainability, vol. 12, no. 19, p. 7915, 2020. http://dx.doi.org/https://doi.org/10.3390/su12197915
  39. W. Sałabun and K. Urbaniak, “A new coefficient of rankings similarity in decision-making problems,” in International Conference on Computational Science, Amsterdam, The Netherlands, 3–5 June 2020. Springer, 2020. http://dx.doi.org/https://doi.org/10.1007/978-3-030-50417-5_47 pp. 632–645.
  40. J. Becker and R. Budziński, “Optimization Procedure of the Multi-parameter Assessment and Bidding of Decision-Making Variants in the Computerized Decision Support System,” in Computational Collective Intelligence. Springer, 2015. http://dx.doi.org/https://doi.org/10.1007/978-3-319-24306-1_18 pp. 182–192.
  41. L. Fabisiak, R. Budziński, K. Szczypiór-Piasecka, and P. Ziętek, “Zastosowanie metody wielokryterialnej do analizy diagnostycznej pacjenta z choroba ̨ zwyrodnieniowa ̨ stawu biodrowego,” Studia Informatica Pomerania, no. 4 (42), pp. 15–25, 2016. http://dx.doi.org/http://dx.doi.org/10.18276/si.2016.42-02